Soft Computing Techniques for Querying XBRL Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Availability of business data is an important aspect of effective financial activities. An easy access to financial information has immense influence on actions and decisions regarding investing, trade and operations of companies and firms. The proposed standard – eXtensible Business Reporting Language (XBRL) – provides a means to create a uniform framework for representing corporate and financial information. XBRL defines an easily interpretable, machine‐readable and XML‐based data format. Its flexibility allows for representing business data using different languages, as well as following different regulation standards. One of important benefits of XBRL is application of XML‐based tools and systems that enable easy preparation, processing and validation of corporate data. It is also possible to use XML‐based storage and query systems. In this paper we propose and describe a concept of soft queries. They provide the users with a human‐friendly interface for interacting with XBRL data. These queries are equipped with linguistic terms (such as large , medium , small ) and linguistic qualifiers ( all , mostly ). Such queries are able to provide the users with results similar to the results obtained when they analyse data themselves. Linguistic terms and qualifiers are represented as fuzzy sets. Fuzzy‐based operations and aggregation operators allow for mimicking a human‐like processing of data. The proposed approach is illustrated with queries executed on an XBRL document. Copyright © 2015 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it